Can Keşmir

Tumor Exome Analysis Reveals Neoantigen-Specific T-Cell Reactivity in an Ipilimumab-Responsive Melanoma

The evidence for T-cell–mediated regression of human cancers such as non–small-cell lung carcinoma, renal cell carcinoma, and—in particular—melanoma after immunotherapy is strong. Anti-CTLA4 (ipilimumab) treatment has been approved for treatment of meta-static melanoma,1 and antibody-mediated blockade of PD-1, a second inhibitory receptor on T cells, has shown highly encouraging results in early clinical trials.2,3 Although the clinical activity of these treatments is apparent, it is still unknown which T-cell reactivities are involved in immunotherapy-induced cancer regression.4 T-cell reactivity against nonmutated tumor-associated self-antigens has been analyzed in patients treated with ipilimumab or with autologous tumor-infiltrating T cells, but the magnitude of the T-cell responses observed has been relatively modest.5,6 In part on the basis of such data, recognition of patient-specific mutant epitopes (hereafter referred to as neoantigens) has been suggested to be a potentially important component.7 A potential involvement of mutated epitopes in T-cell control would also fit well with the observation that the mutation load in sun-exposed melanomas is particularly high.8-10 Intriguingly, on the basis of animal model data, it has recently been suggested that (therapy-induced) analysis of T-cell reactivity against patient-specific neoantigens may be feasible through exploitation of cancer genome data.11,12 However, human data have thus far been lacking. Here we report a case of a patient with stage IV melanoma who exhibited a clinical response to ipilimumab treatment. Cancer exome–guided analysis of T-cell reactivity in this patient revealed reactivity against two neoantigens, including a dominant T-cell response against a mutant epitope of the ATR (ataxia telangiectasia and Rad3 related) gene product that increased strongly after ipilimumab treatment. These data provide the first demonstration (to our knowledge) of cancer exome–guided analysis to dissect the effects of melanoma immunotherapy.

Prediction of human protein function from post-translational modifications and localization features

We have developed an entirely sequence-based method that identifies and integrates relevant features that can be used to assign proteins of unknown function to functional classes, and enzyme categories for enzymes. We show that strategies for the elucidation of protein function may benefit from a number of functional attributes that are more directly related to the linear sequence of amino acids, and hence easier to predict, than protein structure. These attributes include features associated with post-translational modifications and protein sorting, but also much simpler aspects such as the length, isoelectric point and composition of the polypeptide chain.

Clustering Patterns of Cytotoxic T-Lymphocyte Epitopes in Human Immunodeficiency Virus Type 1 (HIV-1) Proteins Reveal Imprints of Immune Evasion on HIV-1 Global Variation

ABSTRACT The human cytotoxic T-lymphocyte (CTL) response to human immunodeficiency virus type 1 (HIV-1) has been intensely studied, and hundreds of CTL epitopes have been experimentally defined, published, and compiled in the HIV Molecular Immunology Database. Maps of CTL epitopes on HIV-1 protein sequences reveal that defined epitopes tend to cluster. Here we integrate the global sequence and immunology databases to systematically explore the relationship between HIV-1 amino acid sequences and CTL epitope distributions. CTL responses to five HIV-1 proteins, Gag p17, Gag p24, reverse transcriptase (RT), Env, and Nef, have been particularly well characterized in the literature to date. Through comparing CTL epitope distributions in these five proteins to global protein sequence alignments, we identified distinct characteristics of HIV amino acid sequences that correlate with CTL epitope localization. First, experimentally defined HIV CTL epitopes are concentrated in relatively conserved regions. Second, the highly variable regions that lack epitopes bear cumulative evidence of past immune escape that may make them relatively refractive to CTLs: a paucity of predicted proteasome processing sites and an enrichment for amino acids that do not serve as C-terminal anchor residues. Finally, CTL epitopes are more highly concentrated in alpha-helical regions of proteins. Based on amino acid sequence characteristics, in a blinded fashion, we predicted regions in HIV regulatory and accessory proteins that would be likely to contain CTL epitopes; these predictions were then validated by comparison to new sets of experimentally defined epitopes in HIV-1 Rev, Tat, Vif, and Vpr.

The role of the proteasome in generating cytotoxic T-cell epitopes: insights obtained from improved predictions of proteasomal cleavage.

Cytotoxic T cells (CTLs) perceive the world through small peptides that are eight to ten amino acids long. These peptides (epitopes) are initially generated by the proteasome, a multi-subunit protease that is responsible for the majority of intra-cellular protein degradation. The proteasome generates the exact C-terminal of CTL epitopes, and the N-terminal with a possible extension. CTL responses may diminish if the epitopes are destroyed by the proteasomes. Therefore, the prediction of the proteasome cleavage sites is important to identify potential immunogenic regions in the proteomes of pathogenic microorganisms (or humans). We have recently shown that NetChop, a neural network-based prediction method, is the best method available at the moment to do such predictions; however, its performance is still lower than desired. Here, we use novel sequence encoding methods and show that the new version of NetChop predicts approximately 10% more of the cleavage sites correctly while lowering the number of false positives with close to 15%. With this more reliable prediction tool, we study two important questions concerning the function of the proteasome. First, we estimate the N-terminal extension of epitopes after proteasomal cleavage and find that the average extension is relatively short. However, more than 30% of the peptides have N-terminal extensions of three amino acids or more, and thus, N-terminal trimming might play an important role in the presentation of a substantial fraction of the epitopes. Second, we show that good TAP ligands have an increased chance of being cleaved by the proteasome, i.e., the specificity of TAP has evolved to fit the specificity of the proteasome. This evolutionary relationship allows for a more efficient antigen presentation.

HLA Alleles Associated with Slow Progression to AIDS Truly Prefer to Present HIV-1 p24

Background The mechanism behind the association between human leukocyte antigen (HLA) molecules and the rate of HIV-1 disease progression is still poorly understood. Recent data suggest that “protective” HLA molecules, i.e. those associated with a low HIV-1 viral load and relatively slow disease progression, tend to present epitopes from the Gag capsid protein. Although this suggests that preferential targeting of Gag delays disease progression, the apparent preference for Gag could also be a side-effect of the relatively high immunogenicity of the protein. Methods and Findings To separate cause and effect, we predicted HIV-1 epitopes from the whole genome of HIV-1, and found that protective HLA alleles have a true preference for the p24 Gag protein, while non-protective HLA alleles preferentially target HIV-1 Nef. In line with this, we found a significant negative correlation between the predicted affinity of the best-binding p24 epitopes and the relative hazard of HIV-1 disease progression for a large number of HLA molecules. When the epitopes targeted by protective HLA alleles were mapped to the known p24 structure, we found that mutations in these epitopes are likely to disturb the p24 dimer structure, which is expected to severely reduce the fitness of the virus. Conclusions Our results suggest that the intrinsic preference of different HLA molecules to present p24 peptides explains why some HLA molecules are more protective than others.

Properties of MHC Class I Presented Peptides That Enhance Immunogenicity

T-cells have to recognize peptides presented on MHC molecules to be activated and elicit their effector functions. Several studies demonstrate that some peptides are more immunogenic than others and therefore more likely to be T-cell epitopes. We set out to determine which properties cause such differences in immunogenicity. To this end, we collected and analyzed a large set of data describing the immunogenicity of peptides presented on various MHC-I molecules. Two main conclusions could be drawn from this analysis: First, in line with previous observations, we showed that positions P4–6 of a presented peptide are more important for immunogenicity. Second, some amino acids, especially those with large and aromatic side chains, are associated with immunogenicity. This information was combined into a simple model that was used to demonstrate that immunogenicity is, to a certain extent, predictable. This model (made available at http://tools.iedb.org/immunogenicity/) was validated with data from two independent epitope discovery studies. Interestingly, with this model we could show that T-cells are equipped to better recognize viral than human (self) peptides. After the past successful elucidation of different steps in the MHC-I presentation pathway, the identification of variables that influence immunogenicity will be an important next step in the investigation of T-cell epitopes and our understanding of cellular immune responses.

Modeling the adaptive immune system

Abstract Motivation: Immunological bioinformatics methods are applicable to a broad range of scientific areas. The specifics of how and where they might be implemented have recently been reviewed in the literature. However, the background and concerns for selecting between the different available methods have so far not been adequately covered. Summary: Before using predictions systems, it is necessary to not only understand how the methods are constructed but also their strength and limitations. The prediction systems in humoral epitope discovery are still in their infancy, but have reached a reasonable level of predictive strength. In cellular immunology, MHC class I binding predictions are now very strong and cover most of the known HLA specificities. These systems work well for epitope discovery, and predictions of the MHC class I pathway have been further improved by integration with state-of-the-art prediction tools for proteasomal cleavage and TAP binding. By comparison, class II MHC binding predictions have not developed to a comparable accuracy level, but new tools have emerged that deliver significantly improved predictions not only in terms of accuracy, but also in MHC specificity coverage. Simulation systems and mathematical modeling are also now beginning to reach a level where these methods will be able to answer more complex immunological questions. Contact: lunde@cbs.dtu.dk Supplementary information: Supplementary data are available at Bioinformatics online.

MR1-restricted MAIT cells display ligand discrimination and pathogen selectivity through distinct T cell receptor usage

MAIT cells can discriminate between pathogen-derived ligands in a clonotype-dependent manner, and the TCR repertoire is distinct within individuals, indicating that the MAIT cell repertoire is shaped by prior microbial exposure.

Heterozygote advantage fails to explain the high degree of polymorphism of the MHC

Major histocompatibility (MHC) molecules are encoded by extremely polymorphic genes and play a crucial role in vertebrate immunity. Natural selection favors MHC heterozygous hosts because individuals heterozygous at the MHC can present a larger diversity of peptides from infectious pathogens than homozygous individuals. Whether or not heterozygote advantage is sufficient to account for a high degree of polymorphism is controversial, however. Using mathematical models we studied the degree of MHC polymorphism arising when heterozygote advantage is the only selection pressure. We argue that existing models are misleading in that the fitness of heterozygotes is not related to the MHC alleles they harbor. To correct for this, we have developed novel models in which the genotypic fitness of a host directly reflects the fitness contributions of its MHC alleles. The mathematical analysis suggests that a high degree of polymorphism can only be accounted for if the different MHC alleles confer unrealistically similar fitnesses. This conclusion was confirmed by stochastic simulations, including mutation, genetic drift, and a finite population size. Heterozygote advantage on its own is insufficient to explain the high population diversity of the MHC.

Amino Acid Similarity Accounts for T Cell Cross- Reactivity and for ''Holes'' in the T Cell Repertoire

Background Cytotoxic T cell (CTL) cross-reactivity is believed to play a pivotal role in generating immune responses but the extent and mechanisms of CTL cross-reactivity remain largely unknown. Several studies suggest that CTL clones can recognize highly diverse peptides, some sharing no obvious sequence identity. The emerging realization in the field is that T cell receptors (TcR) recognize multiple distinct ligands. Principal Findings First, we analyzed peptide scans of the HIV epitope SLFNTVATL (SFL9) and found that TCR specificity is position dependent and that biochemically similar amino acid substitutions do not drastically affect recognition. Inspired by this, we developed a general model of TCR peptide recognition using amino acid similarity matrices and found that such a model was able to predict the cross-reactivity of a diverse set of CTL epitopes. With this model, we were able to demonstrate that seemingly distinct T cell epitopes, i.e., ones with low sequence identity, are in fact more biochemically similar than expected. Additionally, an analysis of HIV immunogenicity data with our model showed that CTLs have the tendency to respond mostly to peptides that do not resemble self-antigens. Conclusions T cell cross-reactivity can thus, to an extent greater than earlier appreciated, be explained by amino acid similarity. The results presented in this paper will help resolving some of the long-lasting discussions in the field of T cell cross-reactivity.